Selecting and Analyzing Speech Features for the Screening of Mild Cognitive Impairment

The total number of patients with Alzheimer's Disease (AD) has exceeded 10 million in China, while the consultation rate is only 14%. Large-scale early screening of cognitive impairment is necessary, however, the methods of traditional screening are expensive and time-consuming. This study explores a speech-based method for the early screening of cognitive impairment by selecting and analyzing speech features to reduce cost and increase efficiency. Specifically, speech-based early screening models are built based on a feature selection method and a self-built dataset including AD patients, Mild Cognitive Impairment (MCI) patients, and healthy controls. This method achieves 10% relative improvement in F1-score to discriminate MCI patients from healthy controls on our dataset. The prediction F1-score reached 70.73% when discriminating MCI patients from healthy controls based on the feature importance list calculated by the auxiliary model that is built to discriminate AD from Control group. Besides, to further assist the medical screening of MCI, we analyze the correlation between brain atrophy features and speech features including acoustic, lexical and duration features. On the basis of key speech feature selection and correlation analysis, the reference interval of speech features is constructed based on the speech data from Control group to provide a reference for evaluating cognitive impairment.Clinical Relevance — We build a speech-based dataset including AD, MCI and Control groups, and provide a feature selection method to improve the effectiveness of the screening of MCI. Apart from this, the correlation between speech features and brain atrophy features is analyzed. Finally, the reference interval of key speech features is established.